Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations152
Missing cells18
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.3 KiB
Average record size in memory136.9 B

Variable types

Numeric11
Categorical4
DateTime2

Alerts

centr_2_turb_cf is highly overall correlated with turb_fin_cultivo_cfHigh correlation
dur_cf is highly overall correlated with orden_encadenado_cfHigh correlation
id_bio is highly overall correlated with loteHigh correlation
id_centr is highly overall correlated with loteHigh correlation
lote is highly overall correlated with id_bio and 3 other fieldsHigh correlation
lote_parental_cf is highly overall correlated with lote and 2 other fieldsHigh correlation
orden_encadenado_cf is highly overall correlated with dur_cf and 2 other fieldsHigh correlation
producto_1_cf is highly overall correlated with producto_2_cfHigh correlation
producto_2_cf is highly overall correlated with producto_1_cfHigh correlation
turb_fin_cultivo_cf is highly overall correlated with centr_2_turb_cf and 1 other fieldsHigh correlation
turb_inicio_cultivo_cf is highly overall correlated with lote_parental_cfHigh correlation
turbidez_diff_cf is highly overall correlated with turb_fin_cultivo_cfHigh correlation
orden_encadenado_cf is highly imbalanced (55.1%)Imbalance
lote_parental_cf is highly imbalanced (72.5%)Imbalance
vol_ino_util_cf has 5 (3.3%) missing valuesMissing
centr_1_turb_cf has 4 (2.6%) missing valuesMissing
centr_2_turb_cf has 9 (5.9%) missing valuesMissing
lote has unique valuesUnique

Reproduction

Analysis started2024-10-12 18:10:38.377987
Analysis finished2024-10-12 18:10:51.716724
Duration13.34 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

lote
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct152
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23323.151
Minimum23019
Maximum24053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:51.770896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23019
5-th percentile23026.55
Q123060.75
median23101.5
Q324003.25
95-th percentile24044.45
Maximum24053
Range1034
Interquartile range (IQR)942.5

Descriptive statistics

Standard deviation416.71493
Coefficient of variation (CV)0.017867008
Kurtosis-0.75084625
Mean23323.151
Median Absolute Deviation (MAD)43
Skewness1.1084266
Sum3545119
Variance173651.33
MonotonicityNot monotonic
2024-10-12T20:10:51.876015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23019 1
 
0.7%
23131 1
 
0.7%
23123 1
 
0.7%
23124 1
 
0.7%
23125 1
 
0.7%
23126 1
 
0.7%
23127 1
 
0.7%
23129 1
 
0.7%
23130 1
 
0.7%
23132 1
 
0.7%
Other values (142) 142
93.4%
ValueCountFrequency (%)
23019 1
0.7%
23020 1
0.7%
23021 1
0.7%
23022 1
0.7%
23023 1
0.7%
23024 1
0.7%
23025 1
0.7%
23026 1
0.7%
23027 1
0.7%
23028 1
0.7%
ValueCountFrequency (%)
24053 1
0.7%
24052 1
0.7%
24051 1
0.7%
24050 1
0.7%
24049 1
0.7%
24047 1
0.7%
24046 1
0.7%
24045 1
0.7%
24044 1
0.7%
24043 1
0.7%

orden_encadenado_cf
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
127 
2
23 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters152
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Length

2024-10-12T20:10:51.963486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-12T20:10:52.025252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 127
83.6%
2 23
 
15.1%
3 2
 
1.3%

lote_parental_cf
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct23
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
nan
130 
23085.0
 
1
23099.0
 
1
23100.0
 
1
23108.0
 
1
Other values (18)
18 

Length

Max length7
Median length3
Mean length3.5789474
Min length3

Characters and Unicode

Total characters544
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)14.5%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan 130
85.5%
23085.0 1
 
0.7%
23099.0 1
 
0.7%
23100.0 1
 
0.7%
23108.0 1
 
0.7%
23109.0 1
 
0.7%
23112.0 1
 
0.7%
23113.0 1
 
0.7%
23119.0 1
 
0.7%
23118.0 1
 
0.7%
Other values (13) 13
 
8.6%

Length

2024-10-12T20:10:52.112040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan 130
85.5%
24010.0 1
 
0.7%
24050.0 1
 
0.7%
24044.0 1
 
0.7%
24041.0 1
 
0.7%
24036.0 1
 
0.7%
24037.0 1
 
0.7%
24031.0 1
 
0.7%
24027.0 1
 
0.7%
24021.0 1
 
0.7%
Other values (13) 13
 
8.6%

Most occurring characters

ValueCountFrequency (%)
n 260
47.8%
a 130
23.9%
0 42
 
7.7%
2 28
 
5.1%
. 22
 
4.0%
1 18
 
3.3%
3 15
 
2.8%
4 15
 
2.8%
5 4
 
0.7%
9 4
 
0.7%
Other values (3) 6
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 260
47.8%
a 130
23.9%
0 42
 
7.7%
2 28
 
5.1%
. 22
 
4.0%
1 18
 
3.3%
3 15
 
2.8%
4 15
 
2.8%
5 4
 
0.7%
9 4
 
0.7%
Other values (3) 6
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 260
47.8%
a 130
23.9%
0 42
 
7.7%
2 28
 
5.1%
. 22
 
4.0%
1 18
 
3.3%
3 15
 
2.8%
4 15
 
2.8%
5 4
 
0.7%
9 4
 
0.7%
Other values (3) 6
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 260
47.8%
a 130
23.9%
0 42
 
7.7%
2 28
 
5.1%
. 22
 
4.0%
1 18
 
3.3%
3 15
 
2.8%
4 15
 
2.8%
5 4
 
0.7%
9 4
 
0.7%
Other values (3) 6
 
1.1%

id_bio
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
14616
34 
14615
30 
13170
29 
14614
27 
13169
22 
Other values (2)
10 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters760
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row14615
2nd row14616
3rd row13170
4th row14614
5th row14615

Common Values

ValueCountFrequency (%)
14616 34
22.4%
14615 30
19.7%
13170 29
19.1%
14614 27
17.8%
13169 22
14.5%
14617 9
 
5.9%
13189 1
 
0.7%

Length

2024-10-12T20:10:52.176973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-12T20:10:52.239568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
14616 34
22.4%
14615 30
19.7%
13170 29
19.1%
14614 27
17.8%
13169 22
14.5%
14617 9
 
5.9%
13189 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 304
40.0%
6 156
20.5%
4 127
16.7%
3 52
 
6.8%
7 38
 
5.0%
5 30
 
3.9%
0 29
 
3.8%
9 23
 
3.0%
8 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 304
40.0%
6 156
20.5%
4 127
16.7%
3 52
 
6.8%
7 38
 
5.0%
5 30
 
3.9%
0 29
 
3.8%
9 23
 
3.0%
8 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 304
40.0%
6 156
20.5%
4 127
16.7%
3 52
 
6.8%
7 38
 
5.0%
5 30
 
3.9%
0 29
 
3.8%
9 23
 
3.0%
8 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 304
40.0%
6 156
20.5%
4 127
16.7%
3 52
 
6.8%
7 38
 
5.0%
5 30
 
3.9%
0 29
 
3.8%
9 23
 
3.0%
8 1
 
0.1%
Distinct103
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-03-21 06:30:00+00:00
Maximum2024-03-25 12:28:00+00:00
2024-10-12T20:10:52.318786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:52.395920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct137
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-03-23 05:30:00+00:00
Maximum2024-03-27 07:51:00+00:00
2024-10-12T20:10:52.471232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:52.547945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vol_ino_util_cf
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)19.7%
Missing5
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean81.458503
Minimum66.4
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:52.617373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum66.4
5-th percentile79.44
Q180
median81.6
Q382.8
95-th percentile84.16
Maximum88
Range21.6
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.248108
Coefficient of variation (CV)0.027598199
Kurtosis13.302453
Mean81.458503
Median Absolute Deviation (MAD)1.6
Skewness-1.7398503
Sum11974.4
Variance5.0539895
MonotonicityNot monotonic
2024-10-12T20:10:52.677079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
80 44
28.9%
82.4 12
 
7.9%
83.2 12
 
7.9%
81.6 11
 
7.2%
82 11
 
7.2%
80.8 7
 
4.6%
84 7
 
4.6%
83.6 6
 
3.9%
81.2 6
 
3.9%
80.4 5
 
3.3%
Other values (19) 26
17.1%
ValueCountFrequency (%)
66.4 1
 
0.7%
76 1
 
0.7%
77.2 1
 
0.7%
77.6 2
 
1.3%
78.4 1
 
0.7%
78.8 1
 
0.7%
79.2 1
 
0.7%
80 44
28.9%
80.4 5
 
3.3%
80.56 1
 
0.7%
ValueCountFrequency (%)
88 1
 
0.7%
87.2 1
 
0.7%
86.4 1
 
0.7%
85.92 1
 
0.7%
85.6 1
 
0.7%
85.2 1
 
0.7%
84.16 3
 
2.0%
84 7
4.6%
83.6 6
3.9%
83.2 12
7.9%

turb_inicio_cultivo_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.036316
Minimum12.56
Maximum44.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:52.740177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12.56
5-th percentile14.8
Q116.4
median17.76
Q318.8
95-th percentile21.796
Maximum44.4
Range31.84
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation3.3008587
Coefficient of variation (CV)0.18301181
Kurtosis28.581161
Mean18.036316
Median Absolute Deviation (MAD)1.16
Skewness4.248607
Sum2741.52
Variance10.895668
MonotonicityNot monotonic
2024-10-12T20:10:52.833187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.84 6
 
3.9%
16.16 5
 
3.3%
16.64 5
 
3.3%
18.32 5
 
3.3%
17.76 5
 
3.3%
18 4
 
2.6%
18.72 4
 
2.6%
15.28 4
 
2.6%
17.6 4
 
2.6%
17.12 4
 
2.6%
Other values (62) 106
69.7%
ValueCountFrequency (%)
12.56 1
0.7%
13.36 1
0.7%
14.08 1
0.7%
14.4 1
0.7%
14.48 1
0.7%
14.56 2
1.3%
14.8 2
1.3%
14.88 2
1.3%
14.96 1
0.7%
15.04 1
0.7%
ValueCountFrequency (%)
44.4 1
0.7%
30.32 2
1.3%
27.04 1
0.7%
26.24 1
0.7%
23.2 1
0.7%
22 1
0.7%
21.84 1
0.7%
21.76 1
0.7%
21.44 1
0.7%
20.8 2
1.3%

turb_fin_cultivo_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.416316
Minimum42.8
Maximum91.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:52.936048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum42.8
5-th percentile59.828
Q169.1
median74.32
Q381.08
95-th percentile87.2
Maximum91.2
Range48.4
Interquartile range (IQR)11.98

Descriptive statistics

Standard deviation8.9408989
Coefficient of variation (CV)0.12014702
Kurtosis1.2364086
Mean74.416316
Median Absolute Deviation (MAD)5.64
Skewness-0.75227869
Sum11311.28
Variance79.939674
MonotonicityNot monotonic
2024-10-12T20:10:53.036062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 7
 
4.6%
83.2 6
 
3.9%
81.6 5
 
3.3%
80.8 4
 
2.6%
85.6 4
 
2.6%
74.4 3
 
2.0%
87.2 3
 
2.0%
69.04 3
 
2.0%
72.48 3
 
2.0%
73.52 2
 
1.3%
Other values (96) 112
73.7%
ValueCountFrequency (%)
42.8 1
0.7%
44.32 1
0.7%
49.36 1
0.7%
49.76 1
0.7%
54.16 1
0.7%
56.48 1
0.7%
56.96 1
0.7%
59.52 1
0.7%
60.08 1
0.7%
60.72 1
0.7%
ValueCountFrequency (%)
91.2 2
 
1.3%
90.4 2
 
1.3%
89.6 1
 
0.7%
88 1
 
0.7%
87.2 3
2.0%
86.4 2
 
1.3%
85.6 4
2.6%
84.8 2
 
1.3%
84 7
4.6%
83.2 6
3.9%

viab_final_cultivo_cf
Real number (ℝ)

Distinct101
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7016579 × 108
Minimum70400000
Maximum3.696 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:53.107566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum70400000
5-th percentile1.1448 × 108
Q11.48 × 108
median1.652 × 108
Q31.922 × 108
95-th percentile2.2828 × 108
Maximum3.696 × 108
Range2.992 × 108
Interquartile range (IQR)44200000

Descriptive statistics

Standard deviation38308279
Coefficient of variation (CV)0.22512327
Kurtosis4.5153204
Mean1.7016579 × 108
Median Absolute Deviation (MAD)20400000
Skewness0.99890815
Sum2.58652 × 1010
Variance1.4675242 × 1015
MonotonicityNot monotonic
2024-10-12T20:10:53.180620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195200000 5
 
3.3%
164000000 4
 
2.6%
145600000 4
 
2.6%
185600000 3
 
2.0%
157600000 3
 
2.0%
158400000 3
 
2.0%
163200000 3
 
2.0%
153600000 3
 
2.0%
156800000 3
 
2.0%
184000000 3
 
2.0%
Other values (91) 118
77.6%
ValueCountFrequency (%)
70400000 1
0.7%
91200000 1
0.7%
95200000 1
0.7%
97600000 1
0.7%
100000000 1
0.7%
101600000 1
0.7%
104000000 1
0.7%
113600000 1
0.7%
115200000 1
0.7%
117600000 1
0.7%
ValueCountFrequency (%)
369600000 1
0.7%
280000000 1
0.7%
262400000 1
0.7%
260000000 1
0.7%
248000000 1
0.7%
240000000 1
0.7%
232000000 1
0.7%
229600000 1
0.7%
227200000 1
0.7%
224000000 1
0.7%

id_centr
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
14246
60 
17825
54 
12912
36 
6379
 
2

Length

Max length5
Median length5
Mean length4.9868421
Min length4

Characters and Unicode

Total characters758
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17825
2nd row14246
3rd row17825
4th row12912
5th row17825

Common Values

ValueCountFrequency (%)
14246 60
39.5%
17825 54
35.5%
12912 36
23.7%
6379 2
 
1.3%

Length

2024-10-12T20:10:53.252182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-12T20:10:53.592808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
14246 60
39.5%
17825 54
35.5%
12912 36
23.7%
6379 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 186
24.5%
2 186
24.5%
4 120
15.8%
6 62
 
8.2%
7 56
 
7.4%
8 54
 
7.1%
5 54
 
7.1%
9 38
 
5.0%
3 2
 
0.3%

centr_1_turb_cf
Real number (ℝ)

MISSING 

Distinct92
Distinct (%)62.2%
Missing4
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean30.067703
Minimum21.28
Maximum168.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:53.656946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21.28
5-th percentile23.096
Q126.44
median28.56
Q330.5
95-th percentile33.44
Maximum168.8
Range147.52
Interquartile range (IQR)4.06

Descriptive statistics

Standard deviation15.167552
Coefficient of variation (CV)0.50444664
Kurtosis67.864839
Mean30.067703
Median Absolute Deviation (MAD)2.04
Skewness8.063087
Sum4450.02
Variance230.05462
MonotonicityNot monotonic
2024-10-12T20:10:53.732474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.84 7
 
4.6%
28.72 5
 
3.3%
29.44 4
 
2.6%
28.56 4
 
2.6%
30.4 4
 
2.6%
26.56 4
 
2.6%
27.44 3
 
2.0%
30.16 3
 
2.0%
31.76 3
 
2.0%
29.52 3
 
2.0%
Other values (82) 108
71.1%
(Missing) 4
 
2.6%
ValueCountFrequency (%)
21.28 1
0.7%
21.52 1
0.7%
21.76 1
0.7%
21.84 1
0.7%
22.08 1
0.7%
22.4 1
0.7%
22.64 1
0.7%
23.04 1
0.7%
23.2 1
0.7%
23.28 1
0.7%
ValueCountFrequency (%)
168.8 1
 
0.7%
142.4 1
 
0.7%
40.9 1
 
0.7%
36.64 1
 
0.7%
34.48 1
 
0.7%
34 1
 
0.7%
33.6 1
 
0.7%
33.44 3
2.0%
33.2 1
 
0.7%
32.72 1
 
0.7%

centr_2_turb_cf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct112
Distinct (%)78.3%
Missing9
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean23.56979
Minimum9.84
Maximum156.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:53.807058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9.84
5-th percentile12.2
Q117.72
median20.72
Q325
95-th percentile37.704
Maximum156.96
Range147.12
Interquartile range (IQR)7.28

Descriptive statistics

Standard deviation17.21646
Coefficient of variation (CV)0.73044604
Kurtosis46.177047
Mean23.56979
Median Absolute Deviation (MAD)3.84
Skewness6.3247727
Sum3370.48
Variance296.4065
MonotonicityNot monotonic
2024-10-12T20:10:53.878337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 4
 
2.6%
21.36 4
 
2.6%
20.8 4
 
2.6%
17.76 3
 
2.0%
19.52 3
 
2.0%
20.88 2
 
1.3%
17.84 2
 
1.3%
22.24 2
 
1.3%
15.36 2
 
1.3%
20.72 2
 
1.3%
Other values (102) 115
75.7%
(Missing) 9
 
5.9%
ValueCountFrequency (%)
9.84 1
0.7%
10.08 1
0.7%
10.4 2
1.3%
11.44 1
0.7%
11.6 2
1.3%
12.16 1
0.7%
12.56 1
0.7%
12.88 1
0.7%
13.2 1
0.7%
13.36 1
0.7%
ValueCountFrequency (%)
156.96 1
0.7%
151.76 1
0.7%
54.8 1
0.7%
49.04 1
0.7%
44.4 1
0.7%
44 1
0.7%
38.4 1
0.7%
37.84 1
0.7%
36.48 1
0.7%
34.48 1
0.7%

producto_1_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct150
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1658.3157
Minimum526.4
Maximum2395.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:53.952658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum526.4
5-th percentile1175.112
Q11466.76
median1675.4
Q31853.798
95-th percentile2140.896
Maximum2395.36
Range1868.96
Interquartile range (IQR)387.038

Descriptive statistics

Standard deviation307.71306
Coefficient of variation (CV)0.18555758
Kurtosis0.45810105
Mean1658.3157
Median Absolute Deviation (MAD)197.08
Skewness-0.34206353
Sum252063.99
Variance94687.327
MonotonicityNot monotonic
2024-10-12T20:10:54.027756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1468.88 2
 
1.3%
1517.92 2
 
1.3%
1747.92 1
 
0.7%
1902.96 1
 
0.7%
1978.16 1
 
0.7%
2117.76 1
 
0.7%
1688.08 1
 
0.7%
2395.36 1
 
0.7%
2155.76 1
 
0.7%
1116.64 1
 
0.7%
Other values (140) 140
92.1%
ValueCountFrequency (%)
526.4 1
0.7%
969.888 1
0.7%
970.8 1
0.7%
988.096 1
0.7%
1096.584 1
0.7%
1101.04 1
0.7%
1116.64 1
0.7%
1151.44 1
0.7%
1194.48 1
0.7%
1198.16 1
0.7%
ValueCountFrequency (%)
2395.36 1
0.7%
2338.56 1
0.7%
2263.2 1
0.7%
2162.48 1
0.7%
2161.12 1
0.7%
2155.76 1
0.7%
2151.536 1
0.7%
2150.576 1
0.7%
2132.976 1
0.7%
2129.92 1
0.7%

producto_2_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1209789
Minimum2.8
Maximum9.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:54.100801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.936
Q15.1
median6.08
Q37.12
95-th percentile8.312
Maximum9.2
Range6.4
Interquartile range (IQR)2.02

Descriptive statistics

Standard deviation1.4079732
Coefficient of variation (CV)0.23002418
Kurtosis-0.57667228
Mean6.1209789
Median Absolute Deviation (MAD)1.04
Skewness-0.0097291881
Sum930.3888
Variance1.9823884
MonotonicityNot monotonic
2024-10-12T20:10:54.173893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.16 6
 
3.9%
6.88 5
 
3.3%
6.56 5
 
3.3%
5.44 5
 
3.3%
5.52 5
 
3.3%
4.48 5
 
3.3%
5.76 4
 
2.6%
6.72 4
 
2.6%
5.28 4
 
2.6%
4.88 4
 
2.6%
Other values (54) 105
69.1%
ValueCountFrequency (%)
2.8 1
 
0.7%
2.96 1
 
0.7%
3.04 1
 
0.7%
3.44 2
1.3%
3.6 1
 
0.7%
3.68 1
 
0.7%
3.76 1
 
0.7%
4.08 2
1.3%
4.16 2
1.3%
4.24 3
2.0%
ValueCountFrequency (%)
9.2 1
 
0.7%
9.12 1
 
0.7%
8.96 1
 
0.7%
8.72 2
1.3%
8.64 1
 
0.7%
8.48 1
 
0.7%
8.4 1
 
0.7%
8.24 1
 
0.7%
8.16 2
1.3%
8.08 4
2.6%

dur_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173236.18
Minimum151200
Maximum193500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:54.249593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum151200
5-th percentile159600
Q1171045
median174150
Q3177300
95-th percentile182700
Maximum193500
Range42300
Interquartile range (IQR)6255

Descriptive statistics

Standard deviation7021.1053
Coefficient of variation (CV)0.040529092
Kurtosis0.60961578
Mean173236.18
Median Absolute Deviation (MAD)3150
Skewness-0.53540339
Sum26331900
Variance49295919
MonotonicityNot monotonic
2024-10-12T20:10:54.349123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172800 15
 
9.9%
176400 11
 
7.2%
178200 9
 
5.9%
174600 7
 
4.6%
169200 6
 
3.9%
171900 5
 
3.3%
180900 4
 
2.6%
171300 4
 
2.6%
175200 4
 
2.6%
162000 4
 
2.6%
Other values (57) 83
54.6%
ValueCountFrequency (%)
151200 1
 
0.7%
156180 1
 
0.7%
157200 1
 
0.7%
157500 1
 
0.7%
158400 2
1.3%
159300 1
 
0.7%
159600 3
2.0%
161100 2
1.3%
162000 4
2.6%
162300 1
 
0.7%
ValueCountFrequency (%)
193500 1
 
0.7%
189000 1
 
0.7%
187200 1
 
0.7%
185700 1
 
0.7%
185400 1
 
0.7%
183300 1
 
0.7%
182700 3
2.0%
181980 1
 
0.7%
181800 3
2.0%
181500 1
 
0.7%

turbidez_diff_cf
Real number (ℝ)

HIGH CORRELATION 

Distinct138
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.38
Minimum24.72
Maximum73.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-10-12T20:10:54.420155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.72
5-th percentile41.688
Q151.04
median56.32
Q363.6
95-th percentile69.592
Maximum73.92
Range49.2
Interquartile range (IQR)12.56

Descriptive statistics

Standard deviation9.1640079
Coefficient of variation (CV)0.16254005
Kurtosis0.77084379
Mean56.38
Median Absolute Deviation (MAD)6.6
Skewness-0.62096502
Sum8569.76
Variance83.979041
MonotonicityNot monotonic
2024-10-12T20:10:54.492167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.48 3
 
2.0%
49.52 3
 
2.0%
65.2 2
 
1.3%
49.36 2
 
1.3%
48.08 2
 
1.3%
52.8 2
 
1.3%
68.16 2
 
1.3%
44.72 2
 
1.3%
54.16 2
 
1.3%
62.96 2
 
1.3%
Other values (128) 130
85.5%
ValueCountFrequency (%)
24.72 1
0.7%
29.84 1
0.7%
30.96 1
0.7%
31.12 1
0.7%
34.96 1
0.7%
37.2 1
0.7%
40.8 1
0.7%
41.6 1
0.7%
41.76 1
0.7%
42.4 1
0.7%
ValueCountFrequency (%)
73.92 1
0.7%
73.28 1
0.7%
72.48 1
0.7%
72.4 2
1.3%
70.64 1
0.7%
70.24 1
0.7%
69.68 1
0.7%
69.52 1
0.7%
69.44 1
0.7%
69.12 1
0.7%

Interactions

2024-10-12T20:10:50.492790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:38.615855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.026997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.818991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.713835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.658801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.803673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.650924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.550452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.472118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:49.597617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.847258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:39.410280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.341770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.165329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.176110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.994363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.148792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.019426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.932950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.799100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:49.938306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.894281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:39.682049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.384125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.208331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.236096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.326753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.199306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.068192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.977334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.860196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:49.984877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.949123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:39.974293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.429672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.262145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.292711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.385752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.251096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.119804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.030162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.912033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.041367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:51.004191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:40.210475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.475937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.314538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.336066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.433927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.297087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.165333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.082270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.960665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.105977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:51.057037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:40.432781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.524036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.365748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.380490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.481527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.355238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.218551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.138260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:49.027359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.168864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:51.110501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:40.644984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.578895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.432188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.430076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.532263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.405303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.268005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.191711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:49.076664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.242268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:51.175692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:40.883204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.639545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.496202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.477601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.588398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.457101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.320374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.272726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:49.135210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.301333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:51.234686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:41.113799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.688394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.547911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.526952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.645089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.509712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.373527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.329912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:49.185965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.361269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:51.296258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:41.595604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.734630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.614089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.572863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.703747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.558358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.424219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.380512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:49.241169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.407999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:51.347034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:41.810286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:42.777045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:43.661224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:44.615481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:45.751732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:46.605345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:47.479924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:48.426942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:49.554132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-12T20:10:50.450298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-12T20:10:54.544394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
centr_1_turb_cfcentr_2_turb_cfdur_cfid_bioid_centrlotelote_parental_cforden_encadenado_cfproducto_1_cfproducto_2_cfturb_fin_cultivo_cfturb_inicio_cultivo_cfturbidez_diff_cfviab_final_cultivo_cfvol_ino_util_cf
centr_1_turb_cf1.0000.356-0.1990.1740.0000.3930.0000.0000.037-0.0190.4070.4740.280-0.004-0.027
centr_2_turb_cf0.3561.000-0.3020.1540.0170.2280.0000.0000.1940.4280.5060.0890.4670.141-0.073
dur_cf-0.199-0.3021.0000.0000.000-0.3340.4500.587-0.135-0.267-0.461-0.370-0.328-0.2010.167
id_bio0.1740.1540.0001.0000.2201.0000.0000.0640.0500.0680.0410.0430.0000.0000.000
id_centr0.0000.0170.0000.2201.0001.0000.0000.0000.0440.1260.0000.0000.0000.2240.000
lote0.3930.228-0.3341.0001.0001.0001.0001.000-0.2200.0360.1940.1950.133-0.082-0.173
lote_parental_cf0.0000.0000.4500.0000.0001.0001.0000.7480.0000.0000.0000.6590.0000.0000.000
orden_encadenado_cf0.0000.0000.5870.0640.0001.0000.7481.0000.0000.1830.0000.4020.0000.0000.270
producto_1_cf0.0370.194-0.1350.0500.044-0.2200.0000.0001.0000.5130.4320.0630.4330.128-0.105
producto_2_cf-0.0190.428-0.2670.0680.1260.0360.0000.1830.5131.0000.4980.0660.4960.079-0.227
turb_fin_cultivo_cf0.4070.506-0.4610.0410.0000.1940.0000.0000.4320.4981.0000.2190.9440.286-0.294
turb_inicio_cultivo_cf0.4740.089-0.3700.0430.0000.1950.6590.4020.0630.0660.2191.000-0.040-0.056-0.103
turbidez_diff_cf0.2800.467-0.3280.0000.0000.1330.0000.0000.4330.4960.944-0.0401.0000.276-0.236
viab_final_cultivo_cf-0.0040.141-0.2010.0000.224-0.0820.0000.0000.1280.0790.286-0.0560.2761.000-0.024
vol_ino_util_cf-0.027-0.0730.1670.0000.000-0.1730.0000.270-0.105-0.227-0.294-0.103-0.236-0.0241.000

Missing values

2024-10-12T20:10:51.430403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-12T20:10:51.589226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-12T20:10:51.679976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

loteorden_encadenado_cflote_parental_cfid_biof_h_inicio_cff_h_fin_cfvol_ino_util_cfturb_inicio_cultivo_cfturb_fin_cultivo_cfviab_final_cultivo_cfid_centrcentr_1_turb_cfcentr_2_turb_cfproducto_1_cfproducto_2_cfdur_cfturbidez_diff_cf
0230191nan146152023-03-21 06:30:00+00:002023-03-23 05:30:00+00:0082.417.2891.20184000000.017825NaNNaN1747.9206.00169200.073.92
1230201nan146162023-03-21 06:30:00+00:002023-03-23 05:30:00+00:0080.418.8091.20181600000.014246NaNNaN1676.1606.56169200.072.40
2230211nan131702023-03-22 06:30:00+00:002023-03-24 05:30:00+00:0066.416.1686.40248000000.017825NaNNaN1928.4968.08169200.070.24
3230221nan146142023-03-22 06:30:00+00:002023-03-24 05:30:00+00:0085.618.4883.20229600000.012912NaNNaN1782.8005.92169200.064.72
4230231nan146152023-03-28 05:27:00+00:002023-03-30 08:00:00+00:0077.617.1274.40132800000.01782526.5620.881861.8402.96181980.057.28
5230241nan146162023-03-28 05:24:00+00:002023-03-30 05:23:00+00:0076.016.5680.80199200000.01424624.5610.402161.1202.80172740.064.24
6230251nan131702023-03-29 05:09:00+00:002023-03-31 05:29:00+00:0077.217.7687.20199200000.01782530.6429.362044.7204.48174000.069.44
7230261nan146142023-03-29 05:29:00+00:002023-03-31 05:38:00+00:0078.818.2481.20206400000.01424626.4811.602263.2003.44173340.062.96
8230271nan146152023-04-04 08:32:00+00:002023-04-06 10:30:00+00:0083.216.8868.08195200000.01424626.249.841407.6804.08179880.051.20
9230281nan146162023-04-04 08:34:00+00:002023-04-06 10:32:00+00:0083.618.5667.20176000000.01782527.2812.161373.2004.72179880.048.64
loteorden_encadenado_cflote_parental_cfid_biof_h_inicio_cff_h_fin_cfvol_ino_util_cfturb_inicio_cultivo_cfturb_fin_cultivo_cfviab_final_cultivo_cfid_centrcentr_1_turb_cfcentr_2_turb_cfproducto_1_cfproducto_2_cfdur_cfturbidez_diff_cf
14224046224041.0131692024-03-11 12:10:00+00:002024-03-13 09:50:00+00:0080.0019.9288.00156800000.01291232.7228.321783.847.84164400.068.08
143240431nan146142024-03-12 06:25:00+00:002024-03-14 07:25:00+00:0080.0019.4469.68132800000.01424630.3216.161254.564.72176400.050.24
144240451nan146162024-03-12 06:25:00+00:002024-03-14 08:15:00+00:0080.0017.5272.48139200000.01291227.8417.761573.525.76179400.054.96
145240441nan131702024-03-16 08:20:00+00:002024-03-18 07:01:00+00:0083.6019.2877.52160800000.01424630.7220.681528.725.44168060.058.24
14624047224044.0131702024-03-18 12:00:00+00:002024-03-20 06:00:00+00:0080.0018.2486.40223200000.01424628.1626.761794.326.64151200.068.16
147240491nan146172024-03-16 08:22:00+00:002024-03-18 07:23:00+00:0083.6018.8872.64164800000.01291230.5617.001342.804.88169260.053.76
148240501nan146142024-03-23 07:57:00+00:002024-03-25 07:28:00+00:0084.1617.7667.60152000000.0637929.4426.641422.803.68171060.049.84
149240511nan131692024-03-23 07:57:00+00:002024-03-25 07:33:00+00:0084.1617.7680.80160800000.01291233.4419.321486.565.52171360.063.04
15024052224050.0146142024-03-25 12:28:00+00:002024-03-27 07:51:00+00:0086.4017.2869.04148000000.01424623.6818.201857.286.00156180.051.76
15124053224051.0131692024-03-25 11:27:00+00:002024-03-27 07:27:00+00:0087.2016.7279.36148000000.01291226.5619.161784.087.20158400.062.64